Nuclear Engineering and Technology

  • 제53권11호
  • /
  • Pages.3563-3579
  • /
  • 2021
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  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Machine learning of LWR spent nuclear fuel assembly decay heat measurements

  • Ebiwonjumi, Bamidele (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Cherezov, Alexey (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Dzianisau, Siarhei (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology) ;
  • Lee, Deokjung (Department of Nuclear Engineering, Ulsan National Institute of Science and Technology)
  • 투고 : 2020.09.29
  • 심사 : 2021.05.28
  • 발행 : 2021.11.25
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초록

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

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과제정보

This work was supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government (MOTIE) (20206510100040)

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